2026 Song Grid to Chip AI Data Centers

The rapid growth of artificial intelligence (AI) computing is transforming data centers into large, dynamic electrical loads, primarily constrained by energy availability and grid-connection capacity. This article explores AI data centers as grid-interactive computing systems, reviewing grid-integration bottlenecks and evolving connection policies that foster new technological trends through spatio-temporal flexibility. It maps the evolution of power-delivery architectures from medium-voltage interfaces to chip-level while discussing advanced technologies like higher-voltage DC distribution, solid-state transformers, and liquid cooling. A three-level stability framework is established, spanning rack-level DC-bus dynamics, facility-level converter interactions, and system-level grid-coupled behavior, connecting dominant instability mechanisms such as constant power load effects and impedance interactions with modeling and mitigation strategies. The findings highlight grid-to-chip co-design as a critical requirement for scalable AI infrastructure, integrating computing workloads, power-delivery systems, energy buffers, and grid operation.